This paper describes a general fuzzy min-max (GFMM) neural network which is a generalization and extension of the fuzzy min-max clustering and classification algorithms of Simpson (1992, 1993). The GFMM method combines supervised and unsupervised learning in a single training algorithm. The fusion of clustering and classification resulted in an algorithm that can be used as pure clustering, pure c...
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We propose a hybrid prediction system of neural network and memory-based learning. Neural network (NN) and memory-based reasoning (MBR) are frequently applied to data mining with various objectives. They have common advantages over other learning strategies. NN and MBR can be directly applied to classification and regression without additional transformation mechanisms. They also have strength in ...
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We present a neural-networks-based knowledge discovery and data mining (KDDM) methodology based on granular computing, neural computing, fuzzy computing, linguistic computing, and pattern recognition. The major issues include 1) how to make neural networks process both numerical and linguistic data in a database, 2) how to convert fuzzy linguistic data into related numerical features, 3) how to us...
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Describes the implementation of a system that is able to organize vast document collections according to textual similarities. It is based on the self-organizing map (SOM) algorithm. As the feature vectors for the documents statistical representations of their vocabularies are used. The main goal in our work has been to scale up the SOM algorithm to be able to deal with large amounts of high-dimen...
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The growing self-organizing map (GSOM) algorithm is presented in detail and the effect of a spread factor, which can be used to measure and control the spread of the GSOM, is investigated. The spread factor is independent of the dimensionality of the data and as such can be used as a controlling measure for generating maps with different dimensionality, which can then be compared and analyzed with...
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Validity interval analysis (VIA) is a generic tool for analyzing the input-output behavior of feedforward neural networks. VIA is a rule extraction technique that relies on a rule refinement algorithm. The rules are of the form Ri→R0 i.e. "if the input of the neural network is in the region Ri, then its output is in the region R0," where regions are...
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This paper focuses on phase analysis to explore the single neuron local arithmetic and logic operations on their input conductances. Based on the analysis of the rational function model of local spatial summation with the equivalent circuits for steady-state membrane potentials, the prototypes spatial summation with the equivalent circuits for steady-state membrane potentials, the prototypes of lo...
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The curse of dimensionality is severe when modeling high-dimensional discrete data: the number of possible combinations of the variables explodes exponentially. We propose an architecture for modeling high-dimensional data that requires resources (parameters and computations) that grow at most as the square of the number of variables, using a multilayer neural network to represent the joint distri...
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Visual exploration has proven to be a powerful tool for multivariate data mining and knowledge discovery. Most visualization algorithms aim to find a projection from the data space down to a visually perceivable rendering space. To reveal all of the interesting aspects of multimodal data sets living in a high-dimensional space, a hierarchical visualization algorithm is introduced which allows the ...
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We propose a method for learning a general statistical inference engine, operating on discrete and mixed discrete/continuous feature spaces. Such a model allows inference on any of the discrete features, given values for the remaining features. Applications are, e.g., to medical diagnosis with multiple possible diseases, fault diagnosis, information retrieval, and imputation in databases. Bayesian...
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Discovery of domain principles has been a major long-term goal for scientists. The paper presents a system called DOMRUL for learning such principles in the form of rules. A distinctive feature of the system is the integration of the certainty factor (CF) model and a neural network. These two elements complement each other. The CF model offers the neural network better semantics and generalization...
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A novel neural network based technique, called “data strip mining” extracts predictive models from data sets which have a large number of potential inputs and comparatively few data points. This methodology uses neural network sensitivity analysis to determine which predictors are most significant in the problem. Neural network sensitivity analysis holds all but one input to a trained ...
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In this paper, a new subband-based classification scheme is developed for classifying underwater mines and mine-like targets from the acoustic backscattered signals. The system consists of a feature extractor using wavelet packets in conjunction with linear predictive coding (LPC), a feature selection scheme, and a backpropagation neural-network classifier. The data set used for this study consist...
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In this paper, we propose a fuzzy auto-associative neural network for principal component extraction. The objective function is based on reconstructing the inputs from the corresponding outputs of the auto-associative neural network. Unlike the traditional approaches, the proposed criterion is a fuzzy mean squared error. We prove that the proposed objective function is an appropriate fuzzy formula...
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The present article is a novel attempt in providing an exhaustive survey of neuro-fuzzy rule generation algorithms. Rule generation from artificial neural networks is gaining in popularity in recent times due to its capability of providing some insight to the user about the symbolic knowledge embedded within the network. Fuzzy sets are an aid in providing this information in a more human comprehen...
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The main advantages of cascade-correlation learning are the abilities to learn quickly and to determine the network size. However, recent studies have shown that in many problems the generalization performance of a cascade-correlation trained network may not be quite optimal. Moreover, to reach a certain performance level, a larger network may be required than with other training methods. Recent a...
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The self-organizing map (SOM) is an excellent tool in exploratory phase of data mining. It projects input space on prototypes of a low-dimensional regular grid that can be effectively utilized to visualize and explore properties of the data. When the number of SOM units is large, to facilitate quantitative analysis of the map and the data, similar units need to be grouped, i.e., clustered. In this...
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A sufficient condition that a region be classifiable by a two-layer feedforward neural net (a two-layer perceptron) using threshold activation functions is that either it be a convex polytope or that intersected with the complement of a convex polytope in its interior, or that intersected with the complement of a convex polytope in its interior or... recursively. These have been called convex recu...
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We explore techniques from statistical machine learning to predict churn and, based on these predictions, to determine what incentives should be offered to subscribers to improve retention and maximize profitability to the carrier. The techniques include legit regression, decision trees, neural networks, and boosting. Our experiments are based on a database of nearly 47000 USA domestic subscribers...
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Multilayer perceptrons with hard-limiting (signum) activation functions can form complex decision regions. It is well known that a three-layer perceptron (two hidden layers) can form arbitrary disjoint decision regions and a two-layer perceptron (one hidden layer) can form single convex decision regions. This paper further proves that single hidden layer feedforward neural networks (SLFN) with any...
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It is well known that dynamic link matching (DLM) is a flexible pattern matching model tolerant of deformation or nonlinear transformation. However, previous models cannot treat severely deformed data pattern in which local features do not have their counterparts in a template pattern. We extend DLM by introducing local linear maps (LLMs). Our model has a reference vector and an LLM for each latti...
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How to efficiently train recurrent networks remains a challenging and active research topic. Most of the proposed training approaches are based on computational ways to efficiently obtain the gradient of the error function, and can be generally grouped into five major groups. In this study we present a derivation that unifies these approaches. We demonstrate that the approaches are only five diffe...
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Aims & Scope

IEEE Transactions on Neural Networks is devoted to the science and technology of neural networks, which disclose significant technical knowledge, exploratory developments, and applications of neural networks from biology to software to hardware.